Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911073223835648 |
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| author | Bai, Qibing Inoue, Sho Wang, Shuai Jiang, Zhongjie Wang, Yannan Li, Haizhou |
| author_facet | Bai, Qibing Inoue, Sho Wang, Shuai Jiang, Zhongjie Wang, Yannan Li, Haizhou |
| contents | Accent normalization converts foreign-accented speech into native-like speech while preserving speaker identity. We propose a novel pipeline using self-supervised discrete tokens and non-parallel training data. The system extracts tokens from source speech, converts them through a dedicated model, and synthesizes the output using flow matching. Our method demonstrates superior performance over a frame-to-frame baseline in naturalness, accentedness reduction, and timbre preservation across multiple English accents. Through token-level phonetic analysis, we validate the effectiveness of our token-based approach. We also develop two duration preservation methods, suitable for applications such as dubbing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17735 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data Bai, Qibing Inoue, Sho Wang, Shuai Jiang, Zhongjie Wang, Yannan Li, Haizhou Audio and Speech Processing Sound Accent normalization converts foreign-accented speech into native-like speech while preserving speaker identity. We propose a novel pipeline using self-supervised discrete tokens and non-parallel training data. The system extracts tokens from source speech, converts them through a dedicated model, and synthesizes the output using flow matching. Our method demonstrates superior performance over a frame-to-frame baseline in naturalness, accentedness reduction, and timbre preservation across multiple English accents. Through token-level phonetic analysis, we validate the effectiveness of our token-based approach. We also develop two duration preservation methods, suitable for applications such as dubbing. |
| title | Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2507.17735 |